Working Group C

Earth Observation capabilities enabled by AI and other Advanced Technologies ♦ Preview Video
(Recording not available)

Description

The current EO applications architecture only supports predefined space assets planning and processing, via predefined workflow, within pre-allocated processing capabilities.  The current SBS EO applications are trying to simulate a full automation loop from order to using science applications. They are mainly bringing science data to EO applications in order to achieve their mission objectives. However, all those applications still use Man in the Loop or Man in the loop to define image quality parameters and in planning orders, de-conflicting order priorities or simply correcting the errors made by ordering users. They still have their own infrastructure per mission and very mission specific computing machine. This cause a substantial mission’s initial and maintenance cost.

In the last decade concepts like virtual missions appeared to allow EO data users to benefit from multiple missions or policies like that of open data. The virtualization is defined not as the use of virtual machines, as per the common IT trend, but as the virtualization of the concept of building the infrastructure itself. However, the current procurement practices where the ground segment is part of the mission doesn’t support the reuse principles of applications or virtualization concept of the infrastructure.

In the current context of open communications, applications availability and big data, automation is not an answer. The answer has to rely on the following objectives that are required to be achieved in EO applications: Self-awareness, Self-control, Self-improvement through learning and Machine to Machine Connectivity.  The achievement in science areas of AI, Machine learning, Big data, Cyber Security and Cloud Computing and other infrastructure improvements are the answer.

The planning process chain and bring application to data processing and Production chains should be the new focus of future SBS application and infrastructure designs.  EO exploitation capability in cloud computing present great opportunities for different SBS missions. The improvement of EO systems through the definition, design and tested demonstration of a platform for mission planning, discovery, access, processing and exploitation of EO data, with validation through Cloud-based processing responds to the increasing user and science needs.

Using AI is important for the support of EO Ground Segment (GS) applications. Use of intelligent agent and virtual reality environment could be used to train astronauts, mission planners, satellite operators and flight controllers on different aspects of the mission is important. Also, train Engineers on the Space mission Systems Engineering.  Automated system troubleshooting and recovery where multiple technologies could be used; Intelligent systems Integrated AI HW on the space asset, Machine learning algorithms/ANN (supervised training or with unsupervised training). Finally, Systems Engineering approaches to EO SBS missions life cycle and design has to change to accommodate the new technologies impact as mentioned above. Cyber security and exploring new technologies to enhance security and protect valuable Earth Observation (EO) data.

Our panel would emphasize future Internet technologies in order to improve EO services by aiming at reducing the costs associated with on-premises deployment, by efficiency of data workflows while meeting data compatibility and access protocols for various clients and users. Also, the discussion will explore on the advancement and the implementation of such technologies in service of SBS EO data.

The panel will consist of presentation addressing the following fields and will animate a discussions around them.

1. Cloud computing and Cyber security for EO data Infrastructure
2. AI in Service of a New System Engineering approach
3. AI Applications for Remote Sensing applications
4. Virtual Mission Operation Centers
5. AI in Service of EO Data Exploitation System

Biography

Following that, Dr. Fawzy continued his research in the domain of AI application in different fields such as telecommunications and health systems. Dr. Fawzy joined the industry in 1998, and worked for multiple companies such as Motorola and Harris in the fields of Systems Engineering and Project Management. In 2005, Dr. Fawzy joined the Canadian Department of Defense as a Systems Engineer and participated in managing the System of Systems engineering cycle of the intelligent Land Command Support System.

In 2011,  Dr. Fawzy joined the Canadian Space Agency (CSA) as a Senior Systems Engineer, he worked as Lead Systems Engineer for RadarSat Constellation Mission Ground Segment. Currently he’s a member of CSA Lunar Gateway Program Systems Engineering Team. In his function, he supports the different Artificial Intelligence activities within the gateway program  as well other CSA AI scientific and industrial initiatives.

Presentations